Pycon Israel 2021

Python bottleneck optimization - progression from lists to cupy arrays
05-02, 15:30–15:55 (Asia/Jerusalem), PyData Track 2

In optimization problems speed is important, but unfortunately python isn't optimized to speed. In this talk I'll show how to use python and optimize bottleneck functions to be as fast as possible using different libraries and methods.

In this talk I'll present how to optimize the running time of a bottleneck function, progressing from using python lists to cupy's arrays. CuPy is a relatively new library that allows running calculations on the GPU using an API similar to NumPy.

I'll cover a few optimization techniques such as vectorized data structures, a-priori calculations and parallel operations.
I will also showcase how to time the function and simple profiling.

Session language


Target audience

Developers, Data Scientists, R&D

I use machine learning in my daily job as an Senior data scientist, and in addition I also compete on I am interested in mostly in high dimensional data and data analysis that require smart preprocessing and data manipulation in order to get the required results. I enjoy solving problems and constantly learning new things.

I have hands-on experience in both R and python for machine-learning, and enjoy learning other languages (Rust, Golang, C)